Laurent Perrinet

CNRS Marseille, Marseille, Provence-Alpes-Côte d'Azur, France 
Visual system, computational neuroscience
"Laurent Perrinet"

Let's admit it: brains are not computers. Indeed, computers are still deceptive compared to perceptual systems. Think for instance about architectures capable of feeding noisy, ambiguous and rapidly varying raw data to your computer. Or think about architecture performing this in an autonomous manner...

To narrow the gap between neuroscience and the theory of sensory processing computations, LP am interested in bridging statistics of geometrical regularities in natural scenes with the properties of neural computations as they are observed in low-level sensory processes or through low-level behavior. For instance, has the predictability of the motion of objects in physical space an impact in the activity of neurons that detect and subsequently on eye movements? What mechanisms are used to learn statistical regularities? What happens if these adaptive mechanisms are dysfunctional?

Mean distance: 14.26 (cluster 29)
Cross-listing: Computational Biology Tree


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Manuel Samuelides grad student 1999-2003 CERT - ONERA
Simon J. Thorpe grad student 1999-2003 CNRS
 (Codirection with Manuel Samuelides)
Guillaume Masson research scientist 2004-2015 Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université, CNRS
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Ravello CR, Perrinet LU, Escobar MJ, et al. (2019) Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports. 9: 456
Damasse JB, Perrinet LU, Madelain L, et al. (2018) Reinforcement effects in anticipatory smooth eye movements. Journal of Vision. 18: 14
Khoei MA, Masson GS, Perrinet LU. (2017) The Flash-Lag Effect as a Motion-Based Predictive Shift. Plos Computational Biology. 13: e1005068
Kremkow J, Perrinet LU, Monier C, et al. (2016) Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1. Frontiers in Neural Circuits. 10: 37
Taouali W, Benvenuti G, Wallisch P, et al. (2015) Testing the Odds of Inherent versus Observed Over-dispersion in Neural Spike Counts. Journal of Neurophysiology. jn.00194.2015
Damasse JB, Madelain L, Perrinet L, et al. (2015) Anticipatory smooth eye movements and reinforcement. Journal of Vision. 15: 1019
Taouali W, Benvenuti G, Chavane F, et al. (2015) A dynamic model for decoding direction and orientation in macaque primary visual cortex. Journal of Vision. 15: 484
Perrinet LU, Bednar JA. (2015) Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports. 5: 11400
Perrinet LU. (2015) Sparse Models for Computer Vision Biologically Inspired Computer Vision: Fundamentals and Applications. 319-346
Montagnini A, Perrinet LU, Masson GS. (2015) Visual Motion Processing and Human Tracking Behavior Biologically Inspired Computer Vision: Fundamentals and Applications. 267-294
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